Solar Power in Cambridge
#######HODP THEME#######
import plotly.graph_objects as go
# HODP colors
monochrome_colors = ['#251616', '#760000', '#C63F3F', '#E28073', '#F1D3CF']
primary_colors = ['#C63F3F', '#F4B436', '#83BFCC', '#455574', '#E2DDDB']
#Colors: monochrome_colors = black: #251616, dark red: #760000, red: #C63F3F, salmon: #E28073, light pink: #F1D3CF
#primary_colors = red: #C63F3F, yellow: #F4B436, turquoise: #83BFCC, navy: #455574, gray: #E2DDDB
# HODP template
theme_hodp = go.layout.Template(
layout=go.Layout(
title = {'font':{'size':24, 'family':"Helvetica", 'color':monochrome_colors[0]}, 'pad':{'t':100, 'r':0, 'b':0, 'l':0}},
font = {'size':18, 'family':'Helvetica', 'color':'#717171'},
xaxis = {'ticks': "outside",
'tickfont': {'size': 14, 'family':"Helvetica"},
'showticksuffix': 'all',
'showtickprefix': 'last',
'showline': True,
'title':{'font':{'size':18, 'family':'Helvetica'}, 'standoff':20},
'automargin': True
},
yaxis = {'ticks': "outside",
'tickfont': {'size': 14, 'family':"Helvetica"},
'showticksuffix': 'all',
'showtickprefix': 'last',
'title':{'font':{'size':18, 'family':'Helvetica'}, 'standoff':20},
'showline': True,
'automargin': True
},
legend = {'bgcolor':'rgba(0,0,0,0)',
'title':{'font':{'size':18, 'family':"Helvetica", 'color':monochrome_colors[0]}},
'font':{'size':14, 'family':"Helvetica"},
'yanchor':'bottom'
},
colorscale = {'diverging':monochrome_colors},
coloraxis = {'autocolorscale':True,
'cauto':True,
'colorbar':{'tickfont':{'size':14,'family':'Helvetica'}, 'title':{'font':{'size':18, 'family':'Helvetica'}}},
}
)
)
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What is photovoltaics?
Photovoltaic solar panels, or PV for short, are what most people think of when they picture solar panels. A module of this type is made up of an array of cells that convert sunlight into direct current electricity. They are usually installed on the roof of a house and feed their electricity into the household grid, providing renewable energy and reducing electricity bills. Solar hot water systems, on the other hand, are somewhat rarer. Instead of making electricity, they use the sun’s heat to warm up water, which you can use for things like showers or heating your home. While solar hot water needs direct sunlight to heat up water, PV also generates electricity in cloudy and snowy weather and actually works even better with cold temperatures, which makes Massachusetts an excellent location for solar systems.
Use our interactive map to find out which Cambridge households use solar power:
import pandas as pd
import folium
# Load data
df = pd.read_csv("Solar_Installations_20241108.csv")
# Define the starting location for the map, centered around the average location
map_center = [df['Latitude'].mean(), df['Longitude'].mean()]
mymap = folium.Map(location=map_center, zoom_start=13)
# Add markers with color based on "SystemType"
for _, row in df.iterrows():
# Choose color based on the "SystemType" value
color = "#760000" if row["SystemType"] == "PV" else "#455574" if row["SystemType"] == "Solar Hot Water" else "#83BFCC"
# Add a circle marker with the chosen color
folium.CircleMarker(
location=[row['Latitude'], row['Longitude']],
radius=3, # Size of the dot
color=color, # Border color of the dot
fill=True,
fill_color=color, # Fill color of the dot
fill_opacity=0.7
).add_to(mymap)
# Add title, subtitle, and data source using HTML and CSS
title_html = '''
<div style="position:fixed; top:10px; left:50px; z-index:1000;
background-color:white; padding:10px; border-radius:8px;
box-shadow:2px 2px 8px rgba(0,0,0,0.2); font-family:Arial, sans-serif;">
<h2 style="margin:0; color:#251616;">Solar Installations Map</h2>
<p style="margin:0; color:#717171;">Solar installations in Cambridge, MA</p>
<small style="color:#717171;">Data Source: <a href="https://data.cambridgema.gov/Energy-and-the-Environment/Solar-Installations/5a85-fb2s/about_data" target="_blank">Cambridge Open Data</a> as of 11/05/2024</small>
</div>
'''
mymap.get_root().html.add_child(folium.Element(title_html))
# Add a legend using HTML
legend_html = '''
<div style="position: fixed;
bottom: 50px; left: 50px; z-index: 1000;
background-color: white;
border: 2px solid grey;
padding: 10px;
border-radius: 8px;
box-shadow: 2px 2px 8px rgba(0,0,0,0.2);
font-family: Arial, sans-serif;">
<h4 style="margin: 0; text-align: center;">Legend</h4>
<div style="margin: 5px 0;">
<div style="background-color: #760000; width: 15px; height: 15px; display: inline-block;"></div>
<span style="margin-left: 8px;">Photovoltaic (PV)</span>
</div>
<div style="margin: 5px 0;">
<div style="background-color: #455574; width: 15px; height: 15px; display: inline-block;"></div>
<span style="margin-left: 8px;">Solar Hot Water</span>
</div>
</div>
'''
mymap.get_root().html.add_child(folium.Element(legend_html))
# Save the map as an HTML file (or display it inline if in a Jupyter Notebook)
mymap.save("interactive_map_with_legend_and_title.html")
mymap
Run to view results
When people started installing solar panels - and why
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.read_csv("Solar_Installations_20241108.csv")
# Ensure 'PermitIssueDate' is parsed as a datetime object
df['PermitIssueDate'] = pd.to_datetime(df['PermitIssueDate'])
# Count occurrences of each SystemType per date
df_counts = df.groupby(['PermitIssueDate', 'SystemType']).size().unstack(fill_value=0)
# Calculate cumulative sum for each SystemType
df_cumulative = df_counts.cumsum().reset_index()
# Melt data for plotting in long format
df_melted = df_cumulative.melt(id_vars='PermitIssueDate', value_vars=['PV', 'Solar Hot Water'],
var_name='SystemType', value_name='CumulativeCount')
df_melted['SystemType'] = df_melted['SystemType'].replace({'PV': 'Photovoltaic'})
# Create line chart
fig = px.line(
df_melted,
x='PermitIssueDate',
y='CumulativeCount',
color='SystemType',
title=(
"Cumulative Number of Installations Over Time by System Type<br>"
"<span style='font-size:14px; color:#717171;'>Source: <a href='https://data.cambridgema.gov/Energy-and-the-Environment/Solar-Installations/5a85-fb2s/about_data' target='_blank'>Cambridge Open Data</a> as of 11/05/2024</span>"
),
labels={
'PermitIssueDate': 'Permit Issue Date', # Custom x-axis label
'CumulativeCount': 'Total Installations in Cambridge', # Custom y-axis label
'SystemType': 'Type of System' # Legend label
},
color_discrete_map={
'Photovoltaic': '#760000', # Custom color for Photovoltaic
'Solar Hot Water': '#455574' # Custom color for Solar Hot Water
}
)
# Update layout with HODP styling and annotations
fig.update_layout(
annotations=[
# Annotation 1: 2008: GWSA
dict(
x='2008-08-01',
y=0.04,
xref='x',
yref='paper',
text="2008:<br>GWSA",
showarrow=True,
arrowhead=2,
ax=0,
ay=-30,
font=dict(size=12, color="black"),
align='center',
arrowcolor="#760000"
),
# Annotation 2: 2010: Solar Curve-Out Program
dict(
x='2010-08-05',
y=0.085,
xref='x',
yref='paper',
text="2010:<br>Solar Curve-Out<br>Program",
showarrow=True,
arrowhead=2,
ax=0,
ay=-60, # Increased length of arrow
font=dict(size=12, color="black"),
align='center',
arrowcolor="#760000"
),
# Annotation 3: 2018: SMART
dict(
x='2018-11-26',
y=0.65,
xref='x',
yref='paper',
text="2018:<br>SMART",
showarrow=True,
arrowhead=2,
ax=0,
ay=-50,
font=dict(size=12, color="black"),
align='center',
arrowcolor="#760000"
),
# Annotation 4: 2021: Climate Change Act
dict(
x='2021-03-26',
y=0.75,
xref='x',
yref='paper',
text="2021:<br>Climate Change<br>Bill",
showarrow=True,
arrowhead=2,
ax=0, # Center arrow
ay=60, # Arrow pointing upwards
font=dict(size=12, color="black"),
align='center',
arrowcolor="#760000"
)
],
plot_bgcolor='white', # Set the background of the plotting area to white
paper_bgcolor='white', # Set the background of the entire figure to white
xaxis=dict(
gridcolor='#E2DDDB',
linecolor='#E2DDDB',
showline=True), # Set gridlines of x-axis to gray
yaxis=dict(gridcolor='#E2DDDB'), # Set gridlines of y-axis to gray
title=dict(
font=dict(size=24, color='#251616', family='Helvetica'), # Styling for the title
x=0.5, # Center the title
xanchor='center',
yanchor='top'
)
)
# Update y-axis to start at 0
fig.update_yaxes(rangemode="tozero")
# Customize hover information
fig.update_traces(mode="lines", hovertemplate="Date: %{x}<br>Installations: %{y}")
# Show the chart
fig.show()
Run to view results
While the solar installations in Cambridge appear to have had a slow start after their invention in the 1950s, their number has grown exponentially since around 2009, approaching a total of 1,000 installations last fall. So, what changed?
In August 2008, Massachusetts signed the Global Warming Solutions Act (GWSA) into law, setting economy-wide greenhouse gas emission reduction goals for the state. In order to help achieve these goals, the Green Communities Act (GCA) was passed in the same month, which, among other things, promotes renewable energies. The act’s aim was to further develop green energy and make it accessible to more people.
Two years later, in 2010, the Solar Carve-Out Program was launched, providing strong incentives for private households to purchase solar systems by securing an ongoing and stable revenue from the PV system, which would offset installation costs and ensure long-term return on the investment. Another major incentive program was introduced in 2018: SMART (Solar Massachusetts Renewable Target). This is a monthly payment that Massachusetts homeowners can receive for 10 years after the installation of PV systems in addition to income from the electricity production itself.
The climate change bill passed in Massachusetts in 2021 also deals with the promotion of solar energy and prioritizes in particular low-income households and nonprofits. Last year, a recent edition of the law also makes it easier to get PV permission in historic districts and wants to explore further areas for expansion with solar panels, such as parking lot structures. The impact of these changes on the number of solar panels will probably become visible in the next few years.
Costs
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import numpy as np
#######HODP THEME#######
import plotly.graph_objects as go
# Load your dataset from the CSV file
df = pd.read_csv('Solar_Installation_Permits_20241108.csv')
# Ensure the 'Issue Date' column is in datetime format
df['Issue Date'] = pd.to_datetime(df['Issue Date'], errors='coerce') # coerce invalid dates to NaT
# Extract the year from the 'Issue Date' column
df['Year'] = df['Issue Date'].dt.year
# Clean the 'Cost of Construction' column:
# Remove dollar signs and commas, then convert to numeric
df['Cost of Construction'] = df['Cost of Construction'].replace({'\$': '', ',': ''}, regex=True)
# Convert 'Cost of Construction' to numeric (forcing errors to NaN for non-convertible values)
df['Cost of Construction'] = pd.to_numeric(df['Cost of Construction'], errors='coerce')
# Drop rows with missing values in 'Year' or 'Cost of Construction' columns
df = df.dropna(subset=['Year', 'Cost of Construction'])
# Add vertical jitter by adding small random noise to the 'Cost of Construction' values
np.random.seed(0) # For reproducibility
vertical_jitter = np.random.normal(scale=20000, size=len(df)) # Adjust scale for more or less vertical spread
# Create a scatter plot (dot plot)
plt.figure(figsize=(14, 8)) # Increased figure size for better spread
plt.scatter(df['Year'], df['Cost of Construction'] + vertical_jitter, color='#C63F3F', alpha=0.5, s=15) # Apply jitter and color from HODP palette
# Set the y-axis to go from 0 to $560,000
plt.ylim(0, 560000)
# Generate y-ticks with increments of 100k
plt.yticks(np.arange(0, 560001, 100000))
# Format the y-axis labels to display values in $ with commas (e.g., $500,000)
def currency_formatter(x, pos):
return f'${x:,.0f}' # Format as currency, no decimal places
plt.gca().yaxis.set_major_formatter(FuncFormatter(currency_formatter))
# Apply HODP color scheme to the grid and axes
plt.grid(True, linestyle='--', alpha=0.7, color='#E2DDDB') # Light gray grid
plt.tick_params(axis='both', which='major', labelsize=14, labelcolor='#717171') # Tick labels color
# HODP Font and Title Customization
plt.title('Cost of Construction vs Year', fontsize=24, fontname='Helvetica', color='#251616', pad=30) # HODP color for title
plt.xlabel('Year', fontsize=18, fontname='Helvetica', color='#251616', labelpad=15)
plt.ylabel('Cost of Construction ($)', fontsize=18, fontname='Helvetica', color='#251616', labelpad=15)
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Cambridge v. Boston
Is solar installation more popular in one city than the other? Is it cheaper to install solar panels in one city versus the other?
***still need to modify code for boston, dataset is the PVinPTS one***
import pandas as pd
import plotly.express as px
# Load data
df = pd.read_csv("Solar_Installations_20241108.csv")
# Ensure 'PermitIssueDate' is parsed as a datetime object
df['PermitIssueDate'] = pd.to_datetime(df['PermitIssueDate'])
# Count occurrences of each SystemType per date
df_counts = df.groupby(['PermitIssueDate', 'SystemType']).size().unstack(fill_value=0)
# Calculate cumulative sum for each SystemType
df_cumulative = df_counts.cumsum().reset_index()
# Melt data for plotting in long format (required by plotly.express for line plots with multiple lines)
df_melted = df_cumulative.melt(id_vars='PermitIssueDate', value_vars=['PV', 'Solar Hot Water'],
var_name='SystemType', value_name='CumulativeCount')
fig = px.line(
df_melted,
x='PermitIssueDate',
y='CumulativeCount',
color='SystemType',
title='Cumulative Number of Installations Over Time by System Type',
labels={
'PermitIssueDate': 'Permit IssueDate', # Custom x-axis label
'CumulativeCount': 'Total Installations', # Custom y-axis label
'SystemType': 'Type of System' # Legend label
},
color_discrete_map={
'PV': '#251616', # Custom color for PV
'Solar Hot Water': '#760000' # Custom color for Solar Hot Water
}
)
# Update layout to start y-axis at 0
fig.update_yaxes(rangemode="tozero")
# Customize hover information
fig.update_traces(mode="lines+markers", hovertemplate="Date: %{x}<br>Installations: %{y}")
# Show plot
fig.show()
Run to view results